activity
20202022
most citedREST: Relational Event-driven Stock Trend Forecasting

47 citations · 67 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2022

Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs

Bowen Tian, Qinliang Su, Jian Yin

The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but t…

cs.LG2021

Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer

Siyuan Liu, Yusong Wang, Tong Wang +6

The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential ro…

cs.LG20215 cited

Instance-wise Graph-based Framework for Multivariate Time Series Forecasting

Wentao Xu, Weiqing Liu, Jiang Bian +2

The multivariate time series forecasting has attracted more and more attention because of its vital role in different fields in the real world, such as finance, traffic, and weathe…

cs.LG202114 cited

Large Scale Private Learning via Low-rank Reparametrization

Da Yu, Huishuai Zhang, Wei Chen +2

We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing indivi…

q-fin.ST202147 cited

REST: Relational Event-driven Stock Trend Forecasting

Wentao Xu, Weiqing Liu, Chang Xu +3

Stock trend forecasting, aiming at predicting the stock future trends, is crucial for investors to seek maximized profits from the stock market. Many event-driven methods utilized…

cs.LG2020

How Does Data Augmentation Affect Privacy in Machine Learning?

Da Yu, Huishuai Zhang, Wei Chen +2

It is observed in the literature that data augmentation can significantly mitigate membership inference (MI) attack. However, in this work, we challenge this observation by proposi…